A successful approach to tracking is to on-line learn discriminative classifiers for the target objects. Although these trackingby-detection approaches are usually fast and accurate they easily drift in case of putative and self-enforced wrong updates. Recent work has shown that classifier-based trackers can be significantly stabilized by applying semi-supervised learning methods instead of sup...

Relevance feedback has been an indispensable component for multimedia retrieval systems. In this paper, we present an adaptive pattern discovery method, which addresses relevance feedback by interactively discovering meaningful patterns of relevant objects. To facilitate pattern discovery, we first present a dynamic feature extraction method, which aims to alleviate the curse of dimensionality ...

As people living near forests in many parts of the world receive recognition of resource-management rights, questions arise about where forest boundaries should be set and who should legitimately receive these rights. Drawing on research conducted among forest-dwelling Kenyah communities in Kalimantan, Indonesia, during 1995 to 1998, I show that the realization of resource rights must be unders...

Random Forests are a powerful classification technique, consisting of a collection of decision trees. One useful feature of Random Forests is the ability to determine the importance of each variable in predicting the outcome. This is done by permuting each variable and computing the change in prediction accuracy before and after the permutation. This variable importance calculation is similar t...

This article describes an example of using machine learning to estimate the abundance of airborne Ambrosia pollen for Tulsa, OK. Twenty-seven years of historical pollen observations were used. These pollen observations were combined with machine learning and a very complete meteorological and land surface context of 85 variables to estimate the daily Ambrosia abundance. The machine learning alg...

Medical data mainly includes data of patients and their associated symptoms. Detecting a disease is becoming costly in terms of money and effort. Medical care will be much better if the predictions can be made with minimal efforts. Predictive modeling will help in detecting a disease early. Medical prediction methods which are computer based will help to improve diagnosis. These methods are the...

The random forest algorithm belongs to the class of ensemble learning methods that are embarassingly parallel, i.e., the learning task can be straightforwardly divided into subtasks that can be solved independently by concurrent processes. A parallel version of the random forest algorithm has been implemented in Erlang, a concurrent programming language originally developed for telecommunicatio...

Random forests (RF) is a popular general purpose classifier that has been shown to outperform many other classifiers on a variety of datasets. The widespread use of random forests can be attributed to several factors, some of which include its excellent empirical performance, scale and unit invariance, robustness to outliers, time and space complexity, and interpretability. While RF has many de...

Random forests (RFs) are recognized as one type of ensemble learning method and are effective for the most classification and regression tasks. Despite their impressive empirical performance, the theory of RFs has yet been fully proved. Several theoretically guaranteed RF variants have been presented, but their poor practical performance has been criticized. In this paper, a novel RF framework ...

In many 3-D object-detection and pose-estimation problems, run-time performance is of critical importance. However, there usually is time to train the system. We introduce an approach that takes advantage of this fact by formulating wide-baseline matching of keypoints extracted from the input images to those found in the model images as a classification problem. This shifts much of the computat...